Grasping virtual fish: A step towards deep learning from demonstration in virtual reality
| Autor: | John Reidar Mathiassen, Jonatan S. Dyrstad |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Deep learning GRASP 02 engineering and technology Virtual reality computer.software_genre Convolutional neural network Robot learning Domain (software engineering) 020901 industrial engineering & automation Human–computer interaction Virtual machine 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Robotics and Biomimetics ROBIO |
| Popis: | We present an approach to robotic deep learning from demonstration in virtual reality, which combines a deep 3D convolutional neural network, for grasp detection from 3D point clouds, with domain randomization to generate a large training data set. The use of virtual reality (VR) enables robot learning from demonstration in a virtual environment. In this environment, a human user can easily and intuitively demonstrate examples of how to grasp an object, such as a fish. From a few dozen of these demonstrations, we use domain randomization to generate a large synthetic training data set consisting of 76 000 example grasps of fish. After training the network using this data set, the network is able to guide a gripper to grasp virtual fish with good success rates. Our domain randomization approach is a step towards an efficient way to perform robotic deep learning from demonstration in virtual reality. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|